GraphEBM: Energy-based graph construction for semi-supervised learning

Zhijie Chen, Hongtai Cao, Kevin Chen Chuan Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


With the rapid improvement of various techniques in graph-based semi-supervised learning, the call for higher-quality graphs becomes more intensive. However, such affinity graphs are not naturally existing in most semi-supervised learning tasks. In this paper, we propose a learning-based approach, GraphEBM, for the graph construction problem. GraphEBM is designed to address three main requirements in graph construction: 1) supporting dynamic update; 2) providing interpretable metrics; 3) tailoring to tasks. Specifically, in GraphEBM, we adopt a probabilistic view, Edge Probability Space, to model a graph construction process as constituted of events from the space. Our objective is thus to learn, by our Energy-Based Model (EBM), the latent sampling distribution. Experimental results show that our proposed GraphEBM outperforms the existing graph construction methods in improving the semi-supervised learning tasks on various datasets and it can learn global properties of a target graph only with direct local guidance.

Original languageEnglish (US)
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728183169
StatePublished - Nov 2020
Event20th IEEE International Conference on Data Mining, ICDM 2020 - Virtual, Sorrento, Italy
Duration: Nov 17 2020Nov 20 2020

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference20th IEEE International Conference on Data Mining, ICDM 2020
CityVirtual, Sorrento


  • Energy-based model
  • Graph construction
  • Graph semi-supervised learning
  • Probability space

ASJC Scopus subject areas

  • General Engineering


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